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Abstract

Numerous studies suggested that long non-coding RNA UCA1 was highly expressed and played critical roles in the development and progression of various cancerous tissues and cells. However, little is known about the association between UCA1 and tumor lymph node metastasis. In our study, a systematic review was conducted to evaluate the association between UCA1 expression and tumor lymph node metastasis and explore whether UCA1 can be a potential molecular marker for predicting the multiple tumor lymph node metastasis. The meta-analysis result showed that the number of lymph node metastasis in different tumorous types of UCA1 high-expression group was significantly higher compared with UCA1 low-expression group (pooled odds ratio = 2.13, 95% confidence interval: 1.60–2.84, p < 0.05). To verify whether the above result was still valid in specific tumor type, we conducted a meta-analysis including four articles on colorectal cancer (pooled odds ratio = 2.07, 95% confidence interval: 1.28–3.34, p < 0.05). Based on the existing results, it can be explained that the long non-coding RNA UCA1 was significantly associated with lymph node metastasis and both the results revealed that compared with UCA1 low-expression group, the lymph node metastasis rate of UCA1 high-expression group was statistically significantly elevated. Therefore, long non-coding RNA UCA1 has the potential of being a biological marker for predicting lymph node metastasis.

Keywords

Long non-coding RNA UCA1 lymph node metastasis meta-analysis

Introduction

As is well known, malignant tumors are of great harm to human health and the trend of death is increasing year by year. Cancers have already become a major cause of morbidity and mortality in most regions worldwide.¹ Nowadays many investigators are searching for biomarkers that may assist the diagnosis or prognosis of cancers.² In the tumorous clinical pathological features, the occurrence of lymph node metastasis (LNM) is a meaningful indicator for survival in most malignancies.³ Some studies have demonstrated that the overall mortality rate was significantly increased and the 5-year overall survival rate did not exceed 50% once the tumor cells spread to the lymph nodes in many types of malignancies.^4-6 Therefore, cancer screening and early diagnosis are of great significance. Identification of common molecular markers for predicting LNM has important clinical application value for the diagnosis and treatment of tumors.

Recently, increasing number of studies have confirmed that long non-coding RNAs (lncRNAs) have emerged as essential regulators in almost all the biological process of tumor initiation, progression, and metastasis.⁷ Long non-coding RNAs are endogenous cellular RNA molecules more than 200 nucleotides in length and lacking the capacity to encode proteins.⁸ Increasing evidences supported that lncRNAs are abnormally expressed in various malignant tumors and are merging as critical components of the cancer transcriptome.⁹ In the process of tumorigenesis and progression of cancers, lncRNAs played a role of oncogenes or suppressor genes.⁸ Urothelial cancer associated 1 (UCA1), mapped to human chromosome 19p13 and included three exons with a length of 1.4 kb, was originally identified in bladder carcinoma cells.¹⁰ Currently, some studies have concluded that UCA1 could regulate cell proliferation, invasion, and metastasis.¹¹ Wang et al.¹² found that upregulated UCA1 contributes to progression of hepatocellular carcinoma through inhibition of miR-216b and activation of fibroblast growth factor receptor 1(FGFR1)/extracellular signal–regulated kinase (ERK) signaling pathway. In bladder cancer cell, UCA1 can promote cell proliferation and metastasis through phosphoinositide 3-kinase (PI3K), Wnt, or Akt signaling pathway.^13–15 Moreover, UCA1 has been reported to be upregulated in several tumor tissues,¹⁶ including bladder, colon, cervix, lung, breast, colorectal, and brain cancer.^10,16–19 Therefore, UCA1 might be feasible as a potential biomarker for predicting tumor LNM. Meta-analysis was a statistical method for the comprehensive evaluation and quantitative analysis of multiple independent research results with the same objectives of the study. To explore the diagnostic value of UCA1 expression associated with LNM in human tumors, we conducted this quantitative meta-analysis.

Methods

Publication search

In this study, meta-analysis was used to evaluate the relationship between UCA1 expression levels and tumor LNM. A comprehensive and accurate literature search of VIP database, Wanfang database, China Knowledge Resource Integrated (CNKI) database, and PubMed database upto May 2016 was performed using the following terms: (“UCA1” or “Urothelial cancer associated 1”) and (“cancer,” “carcinoma,” “tumor,” “tumour,” or “neoplasm”).

Inclusion and exclusion criteria

In this meta-analysis, we have established the following inclusion and exclusion criteria, and the screening process must be strictly observed. Studies were included if they met the following eligibility criteria: (1) studies investigating the relation of UCA1 and cancer patients, (2) the expression levels of UCA1 in primary tumor tissues were measured using quantitative reverse transcription polymerase chain reaction (RT-PCR), (3) patients were grouped according to the expression levels of UCA1, and (4) related clinicopathological parameters were described. Exclusion criteria were the following: (1) letters, editorials, expert opinions, case reports, reviews, and animal studies; (2) data could not be extracted or calculated from the original articles; and (3) duplicate publications.

Data extraction

In order to acquire comprehensive, objective, and true results of meta-analysis, selected literatures and extracted data from the eligible studies were completed independently by two professional training researchers simultaneously and reached a consensus on all items. Disagreements were resolved through discussion with a third investigator. For each study, the following characteristics of the individual research articles were recorded: first author, year of publication, country, tumor types, cases, number of UCA1 high-expression group and low-expression group, number of patients with LNM in each group, and detection methods of UCA1.

Statistical analysis

Statistical analysis of the odds ratios (ORs) for LNM was calculated by Review Manager Version 5.3 (The Cochrane Collaboration, Software Update, Oxford, UK). The heterogeneity of the data was evaluated by chi-square Q test and I² statistic. For the Q test, p value less than 0.05 and I² value greater than 50% indicated significant heterogeneity, using random-effects model. Otherwise, a fixed-effects model was selected. Publication bias was evaluated using funnel plots with Begg’s bias test by Stata 12.0 (Stata Corporation, College Station, TX, USA). For the Begg’s test, p value more than 0.05 explained that funnel plots were symmetric and there is no significant publication bias. Statistical significance was defined as a p value less than 0.05.

Results

Included studies and characteristics

As shown in Figure 1, we searched Chinese and English databases with key terms and revealed 834 articles by May 2016. After the titles and abstracts were reviewed, 798 irrelevant or duplicate studies were excluded. Finally, after excluding 26 articles, 10 studies with a total of 848 patients were included in the current meta-analysis. A total of five different types of cancer were evaluated in this meta-analysis, among these ten articles,^16,20–28 four from colorectal cancer (CRC),^16,20,21,28 two from non–small cell lung cancer (NSCLC),^22,23 two from ovarian cancer (OC),^24,25 one from the gastric cancer (GC),²⁶ and one from esophageal squamous cell carcinoma (ESCC).²⁷ The main characteristics of the included studies were shown in Table 1.

Figure 1.

Flow diagram presenting the steps of literatures search and selection.

Table 1.

Characteristics of studies included in the meta-analysis.

First author	Year	Country	Tumor types	Cases (n)	UCA1 expression				Methods
First author	Year	Country	Tumor types	Cases (n)	High expression	High with LNM	Low expression	Low with LNM	Methods
Zheng	2015	China	GC	112	56	35	56	37	qRT-PCR
Tao	2015	China	CRC	80	20	13	60	21	qRT-PCR
Ni	2015	China	CRC	54	27	12	27	5	qRT-PCR
Bian	2016	China	CRC	90	45	30	45	23	qRT-PCR
Nie	2016	China	NSCLC	112	39	14	73	21	qRT-PCR
Li	2014	China	ESCC	90	41	22	49	12	qRT-PCR
Zhang	2016	China	OC	117	59	26	58	12	qRT-PCR
Han	2014	China	CRC	80	37	17	43	18	qRT-PCR
Wang	2015	China	NSCLC	60	36	26	24	8	qRT-PCR
Yang	2016	China	OC	53	27	13	26	5	qRT-PCR

LNM: lymph node metastasis; GC: gastric cancer; qRT-PCR: quantitative reverse transcription polymerase chain reaction; CRC: colorectal cancer; NSCLC: non–small cell lung cancer; ESCC: esophageal squamous cell carcinoma; OC: ovarian cancer.

Association between UCA1 expression and LNM in different types of tumors

In total, 10 articles reported the number of patients with LNM based on different UCA1 expression levels in a total of 848 patients. The fixed-effects model was adopted as the non-significant heterogeneity (I² = 40%, p = 0.09). Analysis showed a pooled OR = 2.13 (95% confidence interval (CI): 1.60–2.84, p < 0.05; Figure 2). Compared with UCA1 low-expression group, UCA1 high-expression group had a statistically significant elevated LNM rate. The result showed that patients with UCA1 high-expression in tumor tissues were more susceptible to develop LNM.

Figure 2.

Forest plot for the association between UCA1 expression and LNM in different types of tumors.

Association between UCA1 expression and LNM in CRC

Due to the involvement of five different types of the tumors this meta-analysis, we need verify to whether above the result was still valid in specific tumor type. So, we conducted a meta-analysis including four articles about CRC research to explore the relationship between UCA1 expression and LNM in CRC. The fixed-effects model was adopted as the non-significant heterogeneity (I² = 6%, p = 0.37). UCA1 was found to be significantly associated with OR of patients with CRC malignancies (OR = 2.07, 95% CI: 1.28–3.34, p < 0.05; Figure 3). This result revealed that the risk of LNM in the CRC patients with high UCA1 expression was 2.07 times more than that in the UCA1 low-expression group. Thus, it can be said that high UCA1 expression in CRC patients can cause LNM.

Figure 3.

Forest plot for the association between UCA1 expression and LNM in CRC.

In conclusion, our studies illustrated that UCA1 expression was closely related to the neoplasm LNM. The higher the levels of UCA1 expression, the greater the probability of LNM in patients. Therefore, we can conclude that long non-coding RNA UCA1 has the potential to be a biomarker for predicting tumor LNM.

Publication bias

The publication bias of this meta-analysis was evaluated by Begg’s funnel plot test. In the group of different tumor types, the result of Begg’s test (pr > |z| = 0.371) revealed no publication bias (p > 0.05; Figure 4). In the group of CRC, the funnel plot proved that there was no significant asymmetry and publication bias (pr > |z| = 0.734, p > 0.05; Figure 5).

Figure 4.

Funnel plot analysis of potential publications in different types of tumors.

Figure 5.

Funnel plot analysis of potential publications in CRC.

Discussion

Currently, tumors have already become a major cause of morbidity and mortality in most regions worldwide and seriously damaged human health and quality of life. Some studies have clarified that the overall mortality rate was significantly increased and the 5-year overall survival rate was significantly decreased once the tumor cells spread to the lymph nodes in many types of malignancies. Long non-coding RNA UCA1 belongs to the human endogenous retrovirus H (HERV-H) family.²⁹ Liu et al. reported that upregulated UCA1 was associated with poor clinical outcome. UCA1 could serve as a novel biomarker for prognosis and might be a potential predictive factor in various cancers.³⁰ Further studies have also claimed that UCA1 played a potential role in the progression and could serve as a biomarker for diagnosis of bladder cancer.³¹ Moreover, Yang et al. found that UCA1 expression level was upregulated aberrantly in oral squamous cell carcinoma (OSCC) tissues. This demonstrated that UCA1 acts as an oncogene by promoting malignant progression of human OSCC, and it might be related to the activation of the UCA1-β-catenin-WNT signaling pathway regulatory network in OSCC.³² Lu et al. revealed that the expression level of UCA1 in LNM tissue was the highest than that in the proliferative endometrium and primary endometrial cancer (EC) tissues. In addition, after silencing the UCA1, the migration and invasion ability of EC cell lines reduced significantly. This evidence suggested that UCA1 played an important role in the metastasis of EC and may serve as a novel molecular marker to predict the aggressive tumor progression and unfavorable prognosis of EC patients.³³ Though increasing number of studies have focused on the research between UCA1 and tumors, the relationship between UCA1 and LNM in most tumors remained unclear. Therefore, it was urgent to explore the pathogenesis of neoplasm and look for biomarkers for predicting the LNM of tumor patients. In the present meta-analysis, the primary purpose was to determine whether the expression levels of long non-coding RNA UCA1 were related to the incidence of tumor LNM. The second aim was to try to control the confounding factors and to quantify the specific circumstances of the tumor LNM, that is, to determine the increased risk of LNM in UCA1 high-expression group of tumor patients compared with UCA1 low-expression group. Finally, we explored whether UCA1 had the potential to be a biomarker for predicting tumor LNM.

In this study, we found that the UCA1 expression levels in different tumor tissues were correlated with LNM (pooled OR = 2.13, 95% CI: 1.60–2.84, p < 0.05). In addition, meta-analysis in CRC further confirmed the relationship of UCA1 with LNM (pooled OR = 2.07, 95% CI: 1.28–3.34, p < 0.05) and concluded the same outcome as described above. So, this study revealed that the incidence of LNM in patients detected with high UCA1 expression was higher than that in the patients with low UCA1 expression.

At the moment, this is the first meta-analysis study related to lncRNA UCA1 and tumor LNM. This article systematically evaluated the relationship between UCA1 and tumor LNM at home and abroad in recent years, and the time had certain representativeness. Moreover, the inclusion criteria contained in different kinds of tumors met literatures, such as GC, CRC, and NSCLC, and these could strengthen results’ persuasiveness.

Meta-analysis is a systematic analysis with the results of previous studies, so in the process of literatures collection, quality evaluation, selection, and statistical analysis are bound to produce bias. Thus, selected literatures and extracted data from the eligible studies were completed independently by two professional training researchers simultaneously and reached a consensus on all items, and disagreements were resolved through discussion with a third investigator so as to ensure the results to be comprehensive, objective, and true. To assess publication bias in this study, the included studies were conducted using funnel plots and Begg’s test, and the two Begg’s funnel plots were almost symmetric (p > 0.05). Thus, there was no evidence for significant publication bias in this meta-analysis.

However, we also thought that this study may have several deficiencies due to the search scope and limitation of other reasons. Consequently, further research with larger sample size and updated data from each patient is needed to identify unrecognized roles of UCA1 and tumor LNM in different kinds of malignancies.

In summary, the meta-analysis results indicated that UCA1 expression might be a novel prognostic factor for tumor LNM in many types of malignancies. UCA1 may serve as a potential molecular marker for LNM.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the National Natural Science Foundation of China (No. 81102159, 81673267, 81273149, 81473040, and 81302378), National Natural Science Foundation of Guangdong (No. 2016A030313567), Scientific Research Foundation of Guangzhou Municipal Colleges and Universities for Yangcheng Scholar (No. 12A010D), Science and Technology Program of Guangzhou (No. 201607010035), and Foundation for Distinguished Young Talents in Higher Education of Guangdong, China (No. 2013LYM-0072).

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Long non-coding RNA UCA1 can predict tumor lymph node metastasis

Abstract

Keywords

Introduction

Methods

Publication search

Inclusion and exclusion criteria

Data extraction

Statistical analysis

Results

Included studies and characteristics

Association between UCA1 expression and LNM in different types of tumors

Association between UCA1 expression and LNM in CRC

Publication bias

Discussion

Footnotes

Declaration of conflicting interests

Funding

References